(Dataset Exploration Title)

by (your name here)

Preliminary Wrangling

Briefly introduce your dataset here.

Load in your dataset and describe its properties through the questions below. Try and motivate your exploration goals through this section.

What is the structure of your dataset?

81 columns, 113,937 rows, no duplicate value, some of columns needs wrangle.

What is/are the main feature(s) of interest in your dataset?

I am going to draw 9 pictures in Univariate,bivariate and multivariate chapter.

For univariate, I am going to find some characteristics of dataset, including distribution of credit rank, interest rate and borrower's location on the map.

For bivariate visualisation, I am going to dig deeper based on the result above. Such as the different rank's interest rate, the relationship between loan price and interest rate and average loan USD in different states.

For multivariate visualisation, I am going to add more dimension on bivariate visualisation result.

What features in the dataset do you think will help support your investigation into your feature(s) of interest?

Credit grade, borrower rate, borrower state, loan original amount, is borrower home owner, total credit lines past 7 years, delinquencies last 7 years.

Univariate Exploration

In this section, investigate distributions of individual variables. If you see unusual points or outliers, take a deeper look to clean things up and prepare yourself to look at relationships between variables.

Make sure that, after every plot or related series of plots, that you include a Markdown cell with comments about what you observed, and what you plan on investigating next.

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?

There are some unusual points in interests, we can see some incredible low and high interests in the distribution histogram. We may find more about what kind of people can borrow money with cheaper/more expensive price.

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

Firstly, We found there are not enough information in the first pie chart. Credit rank information needs to combine with other variables to find useful information. For geographic information, it gives some useful information, but those information can be reasoning from people's common sense. If we combine this and some other variables, it may gives us better insight(such as average loan, interests, etc.)

Bivariate Exploration

In this section, investigate relationships between pairs of variables in your data. Make sure the variables that you cover here have been introduced in some fashion in the previous section (univariate exploration).

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

Your answer here!

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?

Your answer here!

Multivariate Exploration

Create plots of three or more variables to investigate your data even further. Make sure that your investigations are justified, and follow from your work in the previous sections.

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?

If a person has better credit rank, this means he or she can borrow money with lower interests. People with low interests have to loan less money in higher interest rates. Ownership of a property can also helps. People without property almost cannot borrow money more than 25000 in low interest rate.

Were there any interesting or surprising interactions between features?

I was trying to find the relationship between geographic location and loan status. We can find some interesting result. East/west coast people loan more money than the rest of country, but their credit rank and average bad debt rate are not that low. And we can see some regular patterns on the map, such as some states have very serious bad debt situation and their average loan price is also lower than their neighborhood. We cannot say which one comes first, but we know they must have some relationship.

At the end of your report, make sure that you export the notebook as an html file from the File > Download as... > HTML menu. Make sure you keep track of where the exported file goes, so you can put it in the same folder as this notebook for project submission. Also, make sure you remove all of the quote-formatted guide notes like this one before you finish your report!